1. Field of the Invention
This invention relates to a method and apparatus for automated analysis of digital chest radiographs, and more particularly to automated methods and apparatus for localization of inter-rib spaces for lung texture analysis and detection and characterization of interstitial lung disease in digital chest radiographs.
2. Discussion of Background
A potential advantage of digital radiography is the capability for quantitative analyses of image features representing normal and abnormal patterns, and the subsequent use of these data to aid radiologists' diagnoses. For example, digital image analysis techniques are being developed to detect microcalcifications in mammograms; detect lung nodules in chest radiographs; and track opacified vessels and assess stenotic lesions and blood flow data in angiograms.
The requirements for an automated approach to determine positions of regions of interest (ROIs) for sampling lung textures in a digital chest image are relatively simple. Appropriate ROIs containing lung textures must be free from rib structures, large vessels, and image artifacts. Also, for reasons of practicality, these ROIs must be selected as fast as possible.
There have been many attempts to determine the location of ribs in a chest image. The determination of rib locations is almost equivalent to finding the position of inter-rib spaces which are possibly suitable for sampling lung textures. Wechsler et al., Computer Graphics Image Processing 7, 375-390, 1978, formulated a method to detect posterior and anterior ribs on a chest image by using image processing techniques which included filtering, edge detection, and Hough transforms. An error rate of 10-15% on a small test set of five 256.times.256 chest images was reported with an average computation time of 18 minutes on a DEC PDP 11/45 computer. Other methods are based on the analyses of vertical profiles taken through the lung fields, and attempt to identify rib edge points which can later be fitted with a curve. However, straight-forward edge detection is not adequate because (1) there are very many edges in a chest image and (2) rib edges are not apparent in some cases, especially where interstitial disease is present.
Using statistical tests, DeSousa, Computer Vision, Graphics, and Image Processing 23, 1-14, 129-161, (1983) has presented an automatic rib detection method that works by locating the ribs on a small number of vertical profiles through the lung fields in 400.times.400 posterior/anterior chest images. Using this approach, DeSouza reported satisfactory results but gave no indication of the number of cases used in his investigations. Although some of these approaches to the determination of rib locations may be applicable to locate inter-rib spaces for lung texture analysis, these methods require more computations than are typically necessary.
An automated technique for identification of ROI's would be particularly useful in connection with an automated technique for detection and analysis of interstitial lung disease.
Interstitial lung disease is a common clinical entity. Chest radiography constitutes about 40% of hospital-based X-ray examinations in the United states. Approximately 22% of lung abnormalities seen in chest radiographs at the University of Chicago Medical Center are due to interstitial abnormalities. Interstitial disease is defined as an abnormality of the interstitial compartments of the lung, which may be due to infiltration by inflammatory or neoplastic cells or may be a consequence of the accumulation of fluid or proteinaceous material.
It is recognized that evaluation of diffuse interstitial disease in chest radiographs is one of the most difficult problems in diagnostic radiology. This difficulty is due to (1) the numerous patterns and complex variations that are involved, (2) the lack of firmly established correlation between radiologic and pathologic findings, and (3) variations among radiologists in the terms that they use to describe radiographic patterns, which are not defined objectively. The great proliferation of descriptive adjectives used produces considerable variations in interpretation among individuals, institutions, textbooks, and even by the same individual on different days.
If quantitative computerized methods can be developed which provide objective assessment of lung texture patterns, then this subjectivity could be reduced and the accuracy in radiologic interpretation increased. Investigators have been searching for many years for an automated means of detecting and quantifying the severity of coal workers' pneumoconiosis as well as other forms of pulmonary infiltrates. In order to differentiate a normal lung from a lung with pulmonary fibrosis, Sutton et al, IEEE Trans. Comput. C-21, 667 (1972) devised measures based on the statistical properties of the density distribution on a radiograph. They also measured the frequency content of the Fourier spectrum of the lung texture over a mid-frequency range. Kruger et al., IEEE Trans. Systems, Man and Cybermatics SMC-4:40 (1974) attempted to classify coal workers' pneumoconiosis by using two methods; one of which was a statistical approach in which they used 60 texture measures based on point-to-point variations in reduced gray levels, and the other of which was based on an analysis of the optical Fourier spectrum. Tully et al., Invest. Radiol. 13:298 (1978), used the same statistical method to classify normal lungs, alveolar infiltrates and interstitial infiltrates. Revesz et al., Invest. Radiol. 8:345 (1973), obtained the power spectrum of the lung texture by using the optical Fourier transform in order to distinguish between normal lungs and lungs with interstitial disease. Jagoe et al., British J. Indust. Med 32:267 (1975) and Computer and Biomedical Research 12:1 (1979) employed a method of coding the texture patterns in terms of the directions of the gray-level gradient vector, which was determined by sampling of the chest radiograph at 1.2 mm interval, to investigate the severity of pneumoconiosis.
In the statistical approach, because texture measures were obtained from the pixel values, which were reduced to 8 or 16 gray levels, subtle density variations in a radiograph would have been lost in the case of low-contrast patterns caused by interstitial lung disease. Another problem in previous studies was that texture measures were determined in terms of the density variations, which included the overall lung structure (low-frequency background trend) in the chest radiograph. Thus, previous texture measures were very insensitive to small changes in the fluctuating patterns of the underlying lung texture. Furthermore, investigators failed to demonstrate whether these texture measures corresponded to any features that radiologists normally see in a chest image. Because of these problems, previous attempts to use computer analysis of lung texture for the diagnosis of interstitial disease have not been widely accepted.